10 papers
DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection
Hun Im, Jungi Lee, Subeen Cha +1
In industrial environments, new product categories arrive sequentially, requiring continual anomaly detection without access to past data. Normalizing Flows (NFs) provide exact den…
AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMs
Jaehee Kim, Pilsung Kang
Modern LLMs are increasingly accessed via black-box APIs, requiring users to transmit sensitive prompts, outputs, and fine-tuning data to external providers, creating a critical pr…
Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
Seung Hun Han, Hyeongwon Kang, Jinwoo Park +1
Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresol…
Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoning
Jaeyong Ko, Pilsung Kang, Yukyung Lee
Large language models (LLMs) reach high accuracy in mathematical reasoning, but individual traces on the same problem diverge; some arrive at the correct answer while others fail.…
Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
Hyeongwon Kang, Jeongseob Kim, Jinwoo Park +1
Recent studies have explored large language models for time-series anomaly detection, yet existing approaches often rely on a single general-purpose model to directly infer anomaly…
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Hyeongwon Kang, Jinwoo Park, Seunghun Han +1
Detecting anomalies in time-series data is critical in domains such as industrial operations, finance, and cybersecurity, where early identification of abnormal patterns is essenti…